tf.function makes invalid assumptions about arguments that are Mapping instances. In general, there are no requirements for Mapping instances to have constructors that accept [(key, value)] initializers, as assumed here.

This leads to cryptic exceptions when used with perfectly valid Mapping s

https://github.com/keras-team/keras/blob/master/keras/engine/training.py#L1071-L1077
There are two validation_steps.

I'm clearly overlooking something because I'm very confused.
Right now the default for values_format is .2g with seems not ideal as it formats 110 as 1.1+e01:

"{:.2g}".format(110)

'1.1e+02'

Changing the precision doesn't really help.
There's a pretty easy way to fix this, but not with any standard python formatting from what I can see:
https://stackoverflow.com/qu

https://github.com/aymericdamien/TensorFlow-Examples/blob/master/tensorflow_v2/notebooks/3_NeuralNetworks/convolutional_network_raw.ipynb

trainable_variables = weights.values() + biases.values() doesn't work.
Also if I write trainable_variables = list(weights.values()) + list(biases.values()), I have to turn on tf.enable_eager_execution(), but the training result is wrong, accuracy is ar

Current implementation does sequential sigmoid_out and mul_. We can get better performance by fusing this operations together.

Current Behavior:

The the wiki page APIExample, for the python example, the handle api is is run through the TessBaseAPIDelete funciton if the api failed to be initialized whereas for the C example below, this is not the case.

python:

rc = tesseract.TessBaseAPIInit3(api, TESSDATA_PREFIX, lang)
if (rc):
    te

• face_recognition version:4.1.0
• Python version:3.7
• Operating System:windows 10

Description

I'm having problem to run this program, the error under below

gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
cv2.error: OpenCV(4.1.0) C:\projects\opencv-python\opencv\modules\imgp

Mish is a new novel activation function proposed in this paper.
It has shown promising results so far and has been adopted in several packages including:

• TensorFlow-Addons
• SpaCy (Tok2Vec Layer)
• [Thinc - SpaCy's official NLP based ML

Target Leakage in mentioned steps in Data Preprocessing. Train/test split needs to be before missing value imputation. Else you will have a bias in test/eval/serve.

Sometimes useful, for creating atomic lockfiles (https://github.com/vtjnash/Pidfile.jl) or secure tempfiles: https://github.com/JuliaLang/julia/blob/31bf76f8a1719e07612b9af4196bfc924cf73e21/base/file.jl#L586

The exclusive=true flag (only valid in combination with create=true) should simply map the the underlying O_EXCL option to open (this exists everywhere that Julia runs).

(could a

I'm not sure if XGBoost s model is well calibrated with softmax. It would be nice to have a doc with various experiments including random forest, dart etc.

Alexnet implementation in tensorflow has incomplete architecture where 2 convolution neural layers are missing. This issue is in reference to the python notebook mentioned below.

https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb

What's the ETA for updating the massively outdated documentation?

Please update all documents that are related building CNTK from source with latest CUDA dependencies that are indicated in CNTK.Common.props and CNTK.Cpp.props.
I tried to build from source, but it's a futile effort.

I am having difficulty in running this package as a Webservice. Would appreciate if we could provide any kind of documentation on implementing an API to get the keypoints from an image. Our aim is to able to deploy this API as an Azure Function and also know if it is feasible.

I was going though the existing enhancement issues again and though it'd be nice to collect ideas for spaCy plugins and related projects. There are always people in the community who are looking for new things to build, so here's some inspiration ✨ For existing plugins and projects, check out the spaCy universe.

If you have questions about the projects I suggested,

According to the List_of_unsolved_problems_in_computer_science

Is there any perfect stemming algorithm in the English language?

I believe that lemmatization is not solved too.

It would be wonderful to add the states of the arts in both tasks.
BTW, lemmatization

I recently added "back to top" button to README. What other features would make it easier to browse? Please write your recommendation.